Because current ISR systems lack cross-cueing and cross-tasking capabilities and remain largely un-integrated and platform- and agency-centric, sharing assets and information across such systems is often very difficult, resulting in unsatisfied users, excess cost, and duplication of effort. For example, current mechanisms for task creation/distribution, information collection, information exploitation, and information dissemination tend to be organized around specific platforms, intelligence organizations, and intelligence disciplines (e.g., IMINT, COMINT, ELINT, MASINT, etc.). In addition, current optimization schemes for ISR systems tend to result in optimization only at the discipline or agency level. For example, task creation/distribution typically remains a binary choice where a task/problem is assigned to an intelligence discipline without regard to other potential intelligence disciplines (or combination of disciplines) that could be employed to more efficiently perform a task or solve a problem.
Current ISR systems also rely heavily on manual processes and, thus, often require involvement of large numbers of experienced intelligence specialists. While these intelligence specialists may have significant intelligence knowledge, they may often lack operational knowledge (e.g., they may lack knowledge about what a user or requester actually needs or why the user or requestor is making a request).